In recent years, urgent needs for counting crowds and vehicles have greatly promoted
research of crowd counting and density estimation. Benefiting from the rapid development of …

This paper focuses on crowd counting. As large-scale variations often exist within crowd
images, neither fixed-size convolution kernel of CNN nor fixed-size attentions of recent …

Previous work shows that a better density map representation can improve the performance
of crowd counting. In this paper, we investigate learning the density map representation …

The accuracy of crowd counting in images has improved greatly in recent years due to the
development of deep neural networks for predicting crowd density maps. However, most …

Supervised crowd counting relies heavily on costly manual labeling, which is difficult and
expensive, especially in dense scenes. To alleviate the problem, we propose a novel …

Semi-supervised approaches for crowd counting attract attention, as the fully supervised
paradigm is expensive and laborious due to its request for a large number of images of …

Crowd counting is a fundamental yet challenging task, which desires rich information to
generate pixel-wise crowd density maps. However, most previous methods only used the …

In this paper, we focus on the crowd localization task, a crucial topic of crowd analysis. Most
regression-based methods utilize convolution neural networks (CNN) to regress a density …

Counting dense crowds through computer vision technology has attracted widespread
attention. Most crowd counting datasets use point annotations. In this paper, we formulate …

Multi-view crowd counting has been previously proposed to utilize multi-cameras to extend
the field-of-view of a single camera, capturing more people in the scene, and improve …